The OWASP Top 10 for AI Agents: Your 2026 Security Checklist (ASI Top 10)
Source: Dev.to
The OWASP ASI Top 10 is the new security blueprint for anyone building or deploying autonomous AI agents.
It expands the classic OWASP Top 10 for LLM applications to cover the unique risks introduced by agents that can plan, execute code, and interact with tools and databases.
Why the old OWASP Top 10 isn’t enough
- Agents are autonomous – they dynamically select, plan, and execute a sequence of actions using an LLM “brain” and a set of tools (APIs, interpreters, etc.).
- Excessive agency – an agent that has more power than it needs can amplify even tiny vulnerabilities into system‑wide compromises.
To mitigate this, the ASI Top 10 introduces two core principles:
| Principle | Description |
|---|---|
| Least‑Agency | An extension of the Principle of Least Privilege (PoLP). Agents should receive only the minimum autonomy required to complete their defined task. Autonomy is a feature to be earned, not a default setting. |
| Strong Observability | Provide clear, comprehensive visibility into what agents are doing, why, and which tools they are invoking. Detailed logging of goal state, tool‑use patterns, and decision pathways is mandatory. |
The ASI Top 10 Vulnerabilities
| ASI ID | Vulnerability | The Threat | Mitigation Focus |
|---|---|---|---|
| ASI01 | Agent Goal Hijack | Attacker manipulates the agent’s core objective or decision pathway (the new Indirect Prompt Injection). | Treat all natural‑language inputs as untrusted; use an Intent Capsule pattern. |
| ASI02 | Tool Misuse & Exploitation | Agent uses a legitimate, authorized tool in an unsafe or unintended manner (e.g., using a read tool to exfiltrate a secret). | Zero‑Trust Tooling; define strict, granular, just‑in‑time permissions. |
| ASI03 | Identity & Privilege Abuse | Agent escalates its privileges by abusing its own identity or inheriting credentials. | Zero‑Trust Identity Management; use unique, short‑lived, session‑based credentials. |
| ASI04 | Agentic Supply‑Chain Vulnerabilities | Vulnerabilities inherited from external components (poisoned RAG data, vulnerable tool definitions, pre‑trained models). | Continuous validation of external data sources; maintain an AI‑specific Software Bill of Materials (SBOM). |
| ASI05 | Unexpected Code Execution (RCE) | Agent is tricked into generating and executing malicious code (e.g., a reverse shell). | Mandatory hardware‑enforced, zero‑access sandboxing for all code interpreters. |
| ASI06 | Memory & Context Poisoning | Persistent corruption of the agent’s long‑term memory (vector stores, knowledge graphs). | Cryptographic integrity checks and rigorous sanitisation of all ingested data. |
| ASI07 | Insecure Inter‑Agent Communication | Vulnerabilities in multi‑agent systems allowing message forging or impersonation. | Apply mTLS and digital signing to all agent‑to‑agent communication. |
| ASI08 | Cascading Failures | A small error triggers an uncontrolled, destructive chain reaction in the agent’s workflow. | Implement circuit breakers and transactional rollback capabilities; define safe failure modes. |
| ASI09 | Human‑Agent Trust Exploitation | Attacker manipulates the agent’s output to deceive the human‑in‑the‑loop into approving a malicious action. | Ensure the agent’s reasoning is fully transparent and auditable before human approval. |
| ASI10 | Rogue Agents | Agents operating outside of their intended mandate or control, often due to governance failure or a malicious update. | Implement a robust, auditable kill‑switch mechanism and continuous behavioural monitoring. |
Note: While all ten are important, a few demand immediate architectural attention from developers.
Immediate Developer Actions
1. Guard the Agent’s Goal – Intent Capsule
- Architectural pattern: Bind the agent’s original, signed mandate (the Intent Capsule) to every execution cycle.
- Runtime check: If a new input attempts to change the goal, flag it immediately and abort or require manual review.
Validation – Treat every natural‑language input—whether from a user, a RAG document, or any external source—as untrusted. Route it through rigorous validation before it reaches the planner.
2. Secure Generated Code – Sandbox, Sandbox, Sandbox
- Isolation: Any code generated by an LLM must run in a secure, isolated sandbox.
- Restrictions: The sandbox must have zero network access and limited filesystem access.
- Implementation tip: Prefer hardware‑enforced sandboxing (e.g., Intel SGX, AMD SEV, ARM TrustZone) over pure software solutions.
3. Protect Long‑Term Memory – Data Integrity
- Treat the memory store (vector DB, knowledge graph, etc.) as a highly sensitive database.
- Cryptographic integrity: Apply hash‑based or Merkle‑tree signatures to every chunk of stored data.
- Version control & rollback: Keep immutable versions and enable rapid rollback on detection of tampering.
Closing Thoughts
The OWASP Top 10 for Agentic Applications is a clear call to evolve beyond classic prompt‑injection defenses. Autonomous agents amplify risk, but with Least‑Agency, Strong Observability, and the concrete mitigations outlined above, developers can build safer, more trustworthy AI systems.
Stay vigilant, keep your agents accountable, and secure the future of autonomous AI.
Securing the Future of Autonomous AI
The future of AI is autonomous, but its success depends entirely on our ability to secure it.
For developers, this means shifting your mindset from securing static applications to securing dynamic, privileged, and autonomous entities. Embrace Least‑Agency and Strong Observability as your guiding principles.
- Start by auditing your agents against the ASI Top 10 today.
- The threats are real, and the time to build in the defenses is now.
Question: Which of the ASI Top 10 threats do you think is the most challenging to mitigate in a real‑world multi‑agent system?
Let’s discuss in the comments!